The psychological boundaries of political groups
Bibliographic record
Abstract
Group conflicts, such as political polarization, depend on categorizing the world as “us” (ingroups) versus “them” (outgroups). Previous studies on political groups often measure political ideology on a one-dimensional scale, assuming that it represents two monolithic groups: “liberals” and “conservatives”, divided at the scale midpoint. We directly investigate where individuals see the psychological boundaries of “us” on this scale. We propose instead that political ingroups are based on the extent of political similarity with a given target, not just being on the same half of the ideology scale. We compare the two approaches in studies of Canadian participants, who have a multi-party political system. In online Studies 1 and 2, political distance between targets and participants significantly predicted both ingroup and outgroup categorization, confirming our proposed Distance Model. Participants tolerated further distances for ingroup members on the same half of the spectrum. In an experience sampling study (Study 3), the Distance Model explained more variance in daily social interaction outcomes than the typical approach. Model comparison in each analysis reveals that evidence consistently favored the Distance Model over the typical approach. Political ingroups are thus based on relative political similarity with targets, not just party membership or a left-right divide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".